Comparing Wolves, Particles, Ants, and Orbits for Dimensional Optimization Problem

This paper presents a comparative study of four prominent metaheuristic optimization algorithms, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the recently proposed Three-Body Problem Optimization Algorithm (3BPOA). The evaluation focuses on algorithm performance across low, medium, and high-dimensional problem spaces using the Rastrigin function as the benchmark. Experimental results indicate that GWO consistently achieves superior accuracy, faster convergence, and better computational efficiency across all dimensions. 3BPOA demonstrates increasing effectiveness as dimensionality grows, outperforming PSO and ACO in high-dimensional settings. PSO balances speed and accuracy well, while ACO shows limited scalability and inferior performance.